Artificial intelligence (AI) is key to progress on screening, identifying and researching promising vaccine candidates against COVID-19. The usefulness of AI tools, not surprisingly, hinges on the quality of data. University World News brought this topic to our attention in their article, “AI acceleration of vaccine research hinges on data quality.”
Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it’s up to date. In business, measuring data quality levels can help organizations identify data errors that need to be resolved and assess whether the data in their IT systems is fit to serve its intended purpose. In healthcare, quality issues can be worse in low-resource settings where data may not be collected properly and data management resources – such as advanced software and experts – may not be available.
Improving data quality in healthcare begins by understanding the core tenets of data quality management, the value it offers, and some of the most common problems to avoid.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
